Development of an in-stream migration model for Gammarus pulex L. (Crustacea, Amphipoda) as a tool in river restoration management

Ecological models can act as interesting tools to support decision-making in river restoration management. In particular models which are capable of predicting the habitat requirements of species are of considerable importance to ensure that the planned actions have the desired effects on the aquatic ecosystem. To this end, Artificial Neural Network (ANN) models were tested and optimized for the prediction of the habitat suitability for Gammarus pulex, a relevant indicator species in water quality assessment. Although ANN models are in general quite robust with a rather high predictive reliability, the model performance had to be increased with regard to simulations for river restoration management. In particular, it has been shown that spatial and temporal expert-rules could possibly be included. Migration dynamics of downstream drift and upstream migration of the organisms and migration barriers along the river (weirs, culverted river sections,␣...) might indeed deliver important additional information on the effectiveness of the restoration plans, and also on the timing of the expected effects. In this context, an additional in-stream migration model for Gammarus pulex was developed. This migration model, implemented in a Geographical Information System (GIS), has been used to simulate a practical river restoration scenario for a river in Flanders, Belgium. The case study illustrated that the removal of a weir, at a particular site, resulted in the improvement of the habitat suitability for Gammarus pulex. The ANN models predicted that after restoration the habitat was suitable again for Gammarus pulex. The migration model indicated that the restored parts of the river would be recolonized within about 2 months. In this way, decision makers can have an idea whether and when a restoration option will have a desired effect.

[1]  Annemarie Goedemakers.  Population dynamics of three gammarid species (Crustacea, Amphipoda) in a French chalk stream. Part II. Standing crop , 1981 .

[2]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[3]  Thomas F. Waters,et al.  Interpretation of Invertebrate Drift in Streams , 1965 .

[4]  W. J. Walley,et al.  Neural network predictors of average score per taxon and number of families at unpolluted river sites in Great Britain , 1998 .

[5]  Peter Goethals,et al.  DEVELOPMENT OF A CONCEPT FOR INTEGRATED ECOLOGICAL RIVER ASSESSMENT IN FLANDERS, BELGIUM , 2001 .

[6]  W. Minckley.  Upstream Movements of Gammarus (Amphipoda) in Doe Run, Meade County, Kentucky , 1964 .

[7]  Young-Seuk Park,et al.  Implementation of artificial neural networks in patterning and prediction of exergy in response to temporal dynamics of benthic macroinvertebrate communities in streams , 2001 .

[8]  O. Soderstrom,et al.  Upstream movements of invertebrates in running waters: a review , 1987 .

[9]  Niels De Pauw,et al.  Comparison of Artificial Neural Network (ANN) Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium , 2002, TheScientificWorldJournal.

[10]  D. Borchardt,et al.  Bioindication of chemical and hydromorphological habitat characteristics with benthic macro-invertebrates based on Artificial Neural Networks , 2001, Aquatic Ecology.

[11]  T. Dapper,et al.  The influence of environmental variables on the abundance of aquatic insects: a comparison of ordination and artificial neural networks , 2000, Hydrobiologia.

[12]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[13]  Jason Matthiopoulos,et al.  The use of space by animals as a function of accessibility and preference , 2003 .

[14]  Young-Seuk Park,et al.  Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network. , 2003, Water research.

[15]  Ingrid M. Schleiter,et al.  Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks , 1999 .

[16]  Hugh F. Clifford,et al.  Seasonal Movements of the Mayfly Leptophlebia cupida (Say) in a Brown-Water Stream of Alberta, Canada , 1974 .

[17]  N. De Pauw,et al.  Performance of two artificial substrate samplers for macroinvertebrates in biological monitoring of large and deep rivers and canals in Belgium and The Netherlands , 1994, Environmental monitoring and assessment.

[18]  Graeme D. Ruxton,et al.  Is there really a drift paradox , 2002 .

[19]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[20]  William Gurney,et al.  POPULATION PERSISTENCE IN RIVERS AND ESTUARIES , 2001 .

[21]  J. M. Elliott,et al.  Upstream Movements of Benthic Invertebrates in a Lake District Stream , 1971 .

[22]  D. Macé.  Potential Impact of Road-Stream Crossings (Culverts) on the Upstream Passage of Aquatic Macroinvertebrates , 2002 .

[23]  D. Williams,et al.  The upstream/downstream movement paradox of lotic invertebrates: quantitative evidence from a Welsh mountain stream , 1993 .

[24]  Peter Goethals,et al.  Optimisation of Artificial Neural Network (ANN) model design for prediction of macroinvertebrate communities in the Zwalm river basin (Flanders, Belgium) , 2002 .

[25]  H. Rahmann,et al.  Upstream movements of benthic macroinvertebrates in two different types of fishways in southwestern Germany , 2004, Hydrobiologia.

[26]  Friedrich Recknagel,et al.  Predictive modelling of macroinvertebrate assemblages for stream habitat assessments in Queensland (Australia) , 2001 .

[27]  I. Dimopoulos,et al.  Application of neural networks to modelling nonlinear relationships in ecology , 1996 .

[28]  J. Allan,et al.  Diel activity of Gammarus pulex (Crustacea) in a South Swedish stream: comparison of drift catches vs baited traps , 1989, Hydrobiologia.

[29]  Brendan J. Hicks,et al.  Macroinvertebrates and water quality: a teaching guide , 2002 .

[30]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .

[31]  D. Hughes.  Some Factors Affecting Drift and Upstream Movements of Gammarus Pulex , 1970 .

[32]  Peter Goethals,et al.  A fish-based index of biotic integrity for upstream brooks in Flanders (Belgium) , 2004, Hydrobiologia.

[33]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[34]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[35]  Sovan Lek,et al.  Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .

[36]  Niels De Pauw,et al.  Method for biological quality assessment of watercourses in Belgium , 2004, Hydrobiologia.

[37]  S. Lek,et al.  Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters , 2003 .

[38]  W. Fisher.  Stream Ecology: Structure and Function of Running Waters , 1995 .

[39]  A. Sih,et al.  A Review of the Drift and Activity Responses of Stream Prey to Predator Presence , 1995 .

[40]  S. Pinkster,et al.  Population dynamics of three gammarid species (Crustacea, Amphipoda) in a French chalk stream. Part III. Migration , 1981 .

[41]  Friedrich Recknagel,et al.  Applications of machine learning to ecological modelling , 2001 .

[42]  J. M. Elliott.  A continuous study of the total drift of freshwater shrimps, Gammarus pulex, in a small stony stream in the English Lake District , 2002 .

[43]  Peter Goethals,et al.  Optimization of Artificial Neural Network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders, Belgium) , 2004 .

[44]  L. Hultin.  Upstream movements of Gammarus pulex pulex (Amphipoda) in a South Swedish stream , 1971 .

[45]  J. M. Elliott.  The drift distances and time spent in the drift by freshwater shrimps, Gammarus pulex, in a small stony stream, and their implications for the interpretation of downstream dispersal , 2002 .

[46]  H. B. N. Hynes,et al.  Upstream Movements of the Benthic Invertebrates in the Speed River, Ontario , 1969 .

[47]  George W. Kling,et al.  Stable Isotopes Resolve the Drift Paradox for Baetis Mayflies in an Arctic River , 1993 .

[48]  Sovan Lek,et al.  Application Of Artificial Neural Network Models To Analyse The Relationships Between Gammarus pulex L. (Crustacea, Amphipoda) And River Characteristics , 2005, Environmental monitoring and assessment.

[49]  T. Waters,et al.  Diurnal Periodicity in the Drift of Stream Invertebrates , 1962 .

[50]  Tommy Olsson,et al.  Springtime migration and growth of Parameletus chelifer (Ephemeroptera) in a temporary stream in northern Sweden , 1978 .

[51]  TOT DE DIERKUNDE,et al.  Population dynamics of three gammarid species ( Crustacea , Amphipoda ) in a French chalk stream . Part IV , 2022 .

[52]  Jaimie T A Dick,et al.  The validity of the Gammarus:Asellus ratio as an index of organic pollution: abiotic and biotic influences. , 2002, Water research.

[53]  John E. Brittain,et al.  Invertebrate drift — A review , 1988, Hydrobiologia.